公开(公告)号：US20220197264A1

公开(公告)日：2022-06-23

申请号：US17601503

申请日：2020-03-12

Applicant: ASML NETHERLANDS B.V.

Inventor： Johannes ONVLEE ,  Arnaud HUBAUX

IPC: G05B19/418

Abstract: A method for configuring a semiconductor manufacturing process, the method including: providing an initial prediction model including a plurality of model parameters to one or more remote locations; receiving at least one updated model parameter from the one or more remote locations, the at least one model parameter is updated by training the initial prediction model with local data at the one or more remote locations; determining aggregated model parameters based on the at least one updated model parameter received from the one or more remote locations; and adjusting the initial prediction model based on the aggregated model parameters, the adjusted prediction model being operable to configure the semiconductor manufacturing process.

公开(公告)号：US20220082949A1

公开(公告)日：2022-03-17

申请号：US17423325

申请日：2020-01-09

Applicant: ASML NETHERLANDS B.V.

Inventor： Arnaud HUBAUX ,  Johan Franciscus Maria BECKERS ,  Dylan John David DAVIES ,  Johan Gertrudis Cornelis KUNNEN ,  Willem Richard PONGERS ,  Ajinkya Ravindra DAWARE ,  Chung-Hsun LI ,  Georgios TSIROGIANNIS ,  Hendrik Cornelis Anton BORGER ,  Frederik Eduard DEJONG ,  Juan Manuel GONZALEZ HUESCA ,  Andriy HLOD ,  Maxim PISARENCO

IPC: G03F7/20 ,  G03F1/70 ,  G06F30/392

Abstract: A method for categorizing a substrate subject to a semiconductor manufacturing process including multiple operations, the method including: obtaining values of functional indicators derived from data generated during one or more of the multiple operations on the substrate, the functional indicators characterizing at least one operation; applying a decision model including one or more threshold values to the values of the functional indicators to obtain one or more categorical indicators; and assigning a category to the substrate based on the one or more categorical indicators.

公开(公告)号：US20230341783A1

公开(公告)日：2023-10-26

申请号：US17796434

申请日：2021-01-19

Applicant: ASML NETHERLANDS B.V.

Inventor： Arnaud HUBAUX ,  Patrick WARNAAR ,  Scott Anderson MIDDLEBROOKS ,  Tijmen Pieter COLLIGNON ,  Chung-Hsun LI ,  Georgios TSIROGIANNIS ,  Sayyed Mojtaba SHAKERI

IPC: G03F7/00

CPC classification number: G03F7/705 ,  G03F7/70458 ,  G03F7/70633

Abstract: A method of determining matching performance between tools used in semiconductor manufacture and associated tools is described. The method includes obtaining a plurality of data sets related to a plurality of tools and a representation of the data sets in a reduced space having a reduced dimensionality. A matching metric and/or matching correction is determined based on matching the reduced data sets in the reduced space.

公开(公告)号：US20230273529A1

公开(公告)日：2023-08-31

申请号：US18012222

申请日：2021-06-14

Applicant: ASML NETHERLANDS B.V.

Inventor： Satej Subhash KHEDEKAR ,  Henricus Jozef CASTELIJNS ,  Anjan Prasad GANTAPARA ,  Stephen Henry BOND ,  Seyed Iman MOSSAVAT ,  Alexander YPMA ,  Gerald DICKER ,  Ewout Klaas STEINMEIER ,  Chaoqun GUO ,  Chenxi LIN ,  Hongwei CHEN ,  Zhaoze LI ,  Youping ZHANG ,  Yi ZOU ,  Koos VAN BERKEL ,  Joost Johan BOLDER ,  Arnaud HUBAUX ,  Andriy Vasyliovich HLOD ,  Juan Manuel GONZALEZ HUESCA ,  Frans Bernard AARDEN

IPC: G03F7/20

CPC classification number: G03F7/70525 ,  G03F7/70633 ,  G03F7/7065

Abstract: Generating a control output for a patterning process is described. A control input is received. The control input is for controlling the patterning process. The control input includes one or more parameters used in the patterning process. The control output is generated with a trained machine learning mod& based on the control input, The machine learning model is trained with training data generated from simulation of the patterning process and/or actual process data, The training data includes 1) a plurality of training control inputs corresponding to a plurality of operational conditions of the patterning process, where the plurality of operational conditions of the patterning process are associated with operational condition specific behavior of the patterning process over time, and 2) training control outputs generated using a physical model based on the training control inputs.

公开(公告)号：US20230333482A1

公开(公告)日：2023-10-19

申请号：US18212334

申请日：2023-06-21

Applicant: ASML NETHERLANDS B.V.

Inventor： Arnaud HUBAUX ,  Johan Franciscus Maria Beckers ,  Dylan John David Davies ,  Johan Gertrudis Cornelis Kunnen ,  Willem Richard Pongers ,  Ajinkya Ravindra Daware ,  Chung-Hsun Li ,  Georgios Tsirogiannis ,  Hendrik Cornelis Anton Borger ,  Frederik Eduard De Jong ,  Juan Manuel Gonzalez Huesca ,  Andriy Hlod ,  Maxim Pisarenco

IPC: G03F7/20 ,  G06F30/392 ,  G03F1/70

CPC classification number: G03F7/70508 ,  G06F30/392 ,  G03F1/70 ,  G03F7/70616 ,  G05B2219/45028 ,  G05B2219/45031

Abstract: A method for categorizing a substrate subject to a semiconductor manufacturing process including multiple operations, the method including: obtaining values of functional indicators derived from data generated during one or more of the multiple operations on the substrate, the functional indicators characterizing at least one operation; applying a decision model including one or more threshold values to the values of the functional indicators to obtain one or more categorical indicators; and assigning a category to the substrate based on the one or more categorical indicators.

公开(公告)号：US20230124106A1

公开(公告)日：2023-04-20

申请号：US17910086

申请日：2021-02-24

Applicant: ASML NETHERLANDS B.V.

Inventor： Arnaud HUBAUX

IPC: G05B13/04 ,  G05B13/02

Abstract: A method including: obtaining one or more models configured for predicting a process metric of a manufacturing process based on inputting process data; and using a reinforcement learning framework to evaluate the one or more models and/or model configurations of the one more models based on inputting new process data to the one or more models and determining a performance indication of the one or more models and/or model configurations in predicting the process metric based on inputting the new process data.